![]() ![]() ![]() Still, the OCR technology has an increasingly strong potential in deep learning applications to build tools for reading license plates on vehicles, digitizing invoices or menus, scanning ID cards, comparing claim forms, and so on. This happens when the text is available in images representing natural environments, geometrical distortions, too much noise or cluttered and complex backgrounds, and different fonts other than the regular ones. OCR-equipped systems can flag any anomalies in the data to the concerned teams and prevent possible fraud.Įven though OCR can easily extract text from images, it sometimes faces challenges. With OCR, it is easier to compare the insurance claim with the policyholder’s details. It saves paper and storage space as more data can be converted to electronic formatĪ typical example of an OCR application can be seen in medical insurance claim form processing. Reduced manual data entry indicates reduced overall costs for the business It saves time and reduces the scope of manual errorsĮliminates the requirement for manual data entry OCR also offers the following benefits –Īutomated, faster processing and conversion of paper-based documents into digital formats that accelerate workflows This is crucial for businesses as they have to deal with media and content daily. This additional layer can be easily read by a computer, thus making the image recognizable and searchable. An OCR tool processes the image to identify the text and creates a hidden layer of text behind the image. This is because by using OCR, we can create digital documents that can be edited and stored per requirements. Why do we need to extract Text from Images?Īs mentioned in the above section, the primary benefit of OCR technology is that it automates manual and time-consuming data entry tasks. In this article, we will discuss OCR, the benefits of OCR, why we need text extraction from documents, OCR libraries available in Python, and an example of text extraction from an image using the Keras-OCR library in Python. Thankfully, many free and commercial tools (offline and online) allow OCR technology to extract text from images.Ĭurrently, OCR tools are pretty advanced due to the implementation of techniques such as intelligent character recognition (ICR), which can identify languages, handwriting styles, etc. In a simpler sense, OCR converts digital data in image format into editable word processing documents. Due to this, the extracted text can be selected, edited, or copy-pasted like regular text. Later these can reconstruct the extracted text in a machine-readable format. These tools are trained to identify the shapes of characters or numbers on an image to recognize the text in the image. For text extraction, the OCR tools (OCR libraries) employ several machine algorithms for pattern recognition to identify the presence and layout of the text in an image file. An OCR system uses a combination of hardware, such as optical scanners and software capable of image processing. An OCR program is a tool that extracts and re-purposes data from scanned documents, camera images, and image-only pdf. Commonly known as ‘Text Recognition,’ it is a popular technique for extracting text from images. The acronym ‘OCR’ stands for Optical Character Recognition. This is possible using OCR or Optical Character Recognition. We all must have used online or offline tools to convert images to editable text formats to make things easier. Such files cannot be edited directly, and there is a need to make them editable first or have a tool that can read the content from the image and extract it for further processing. Handling such data manually in these files is tedious, time-consuming, and prone to manual errors. IntroductionĪlthough plenty of digital information is available for consumption by businesses, employees still have to handle printed invoices, flyers, brochures, and forms in hard copies or textual images saved in. This article was published as a part of the Data Science Blogathon. ![]()
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